Github Repository for the Streamlit "AI and Data Science examples" HEC Paris app.

Deploy the app locally using streamlit run main_page.py.

The app currently has four use cases:

• Time Series Forecasting on Electrical Power Consumption
• Sentiment Analysis on Customer reviews
• Recommendation system for movies

• Clone the repository locally (git clone)
• Create a virtual environment python -m venv <venv_name> ⇒ never push venv folder to github
• Install all the package dependencies using pip install -r requirements.txt
• Create a branch for each feature/use cases added
• Don’t push to the main branch, create a pull request to integrate changes to the repo
• Never push .streamlit folder to the repo
• Make sure your code is well documented (doc strings for functions,…)

• To deploy the app locally, run on the terminal streamlit run main_page.py
• Create a page for each use case (in the page\ folder)
• Add datasets to the data\ folder
• Add pretrained/saved models to the pretrained_models folder
• .streamlit : Add API keys, identifications… to a .yaml file (never pushed to the repo, add the “secrets” to the deployed app directly on the website)